Seismic datasets often contain complex patterns that are subtle and manifested in multiple seismic or attribute/derivative volumes and at multiple spatial scales. Over the last several decades, geologists and geophysicists have developed a range of techniques to extract many important patterns that indicate the presence of hydrocarbons. However, most of these methods involve searching for either known or loosely defined patterns with pre-specified characteristics in one data volume, or two volumes at the most. These “template-based” or “model-based” approaches often miss subtle or unexpected anomalies that do not conform to such specifications. These approaches will not be discussed further here as they have little in common with the present invention except that they address the same technical problem.
Most of these known methods involve a human interpreter searching for either known or loosely defined patterns with pre-specified characteristics in one data volume, or two volumes at the most. These “template-based” or “model-based” approaches often miss subtle or unexpected anomalies that do not conform to such specifications. It is therefore desirable to develop statistical analysis methods that are capable of automatically highlighting anomalous regions in one or more volumes of seismic data across multiple spatial scales without a priori knowledge of what they are and where they are. The present invention satisfies this need.